The Moneyball Approach to Big Data

Written by Rick Kawamura, Vice President of Marketing at Kapow, ‘Big Data’, a term used to describe the explosive growth of data used by organizations of all sizes to differentiate and strategically grow their business, is becoming a big cross-industry headliner. Thanks to on-going technological innovations, such as the proliferation of cloud applications, social media and user-generated content, ubiquitous use of mobile devices, and the availability of various sensor devices – the overall volume of digital content is set to increase by 48 percent this year from 2011. Additionally, more than 90 percent of this information will be unstructured , sitting outside of normal reporting models. In fact, according to IBM research 90 percent of all data was created in the last two years with 2.5 quintillion bytes of data being created every day. While the numbers are staggering, this data deluge is currently presenting both a challenge and an opportunity. Businesses are quickly recognising the huge value of Big Data but at the same time finding it difficult to discover, collect, manage and exploit this unstructured information coming from many disparate sources, so it becomes workable and delivers useful insights.

There are numerous examples of where Big Data, when utilised and analysed effectively, can inform decision making and make a significant impact. One of the more known ones is the movie “Moneyball”. Based on the true fascinating story, the film shows how the low-budget Oakland A’s major league baseball team leveraged data analytics to extract intelligence and competitive advantage from years of historical data, to build a championship team. It is exactly this type of approach that companies need to adopt to make use of the internal and external data they have within their reach – a business strategy especially relevant for the financial services industry where Big Data can be turned into better and more profitable insight into customers, channels and risks.

Big Opportunity in Financial Services and Banking

Organizations within the financial services and banking industries have amassed a wealth of information, capturing billions of financial transactions, customer interactions, demographic information, social media sentiment data and data relating to financial and purchasing behaviours. Left unstructured, this data can appear unworkable and overwhelming, however if leveraged in the right way it has the power to change businesses. This could be by increasing profitability through process optimisation, or by growing sales with predictive analytics based on buying behaviours or alternatively, saving costs by foreseeing changes in market conditions. This type of strategic gain is significant enough to separate winners from losers in most industries and financial services companies are achieving business advantage by mining and analysing data to stay ahead of the competition.

Finance experts already understand that data has value. It’s the lifeblood of their industry, but Big Data has led to a tidal wave of information coming from sources that were never available before including social, web and cloud-based applications. This influx of information can very well shape investment strategies, transform customer acquisition and retention, reduce risk and even create new lines of businesses. However, banks now need to figure out how to effectively store, mine and integrate this unstructured data, as this kind of data will soon be a primary way to glean an accurate profile of customers or identify unique actionable predictors for trading ahead of their competition. Most banks now realise the importance of managing Big Data. Despite the loss of the personal interaction via branch banking,effective analytics means banks can utilise data from multiple sources (transactions, product usage, online banking behaviour, social media, forums, etc.), to gain a deep 360-degree customer intelligence – gaining knowledge of their channel preferences, likes, dislikes and propensities for products and services.Obtaining such a detailed view of a customer is especially crucial in today’s business climate, in which customers expect a personalised experience with targeted communications and relevant offers. By using high-performance analytics, banks can achieve better operational efficiency, deliver higher value to customers, improve risk management and ultimately differentiate and innovate to stand out in the marketplace.

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At the recent American Banker Best Practices in Retail Financial Services Symposium,mining Big Data was identified as a strategic and innovative way to improve interactions with customers, moving away from what customers view as “offer overload” to achieving a level of customer experience excellence that can only be obtained when customers see the perfect offer or communication at exactly the time when they want and need it.

Not just big data In reality, despite all this big promise, many companies are taking a wait-and-see approach because of the enormity and complexity surrounding Big Data. According to Gartner Predicts 2012 research, more than 85 percent of Fortune 500 organisations will be unable to effectively exploit Big Data by 2015. What often gets lost in the Big Data discussion is that, in order to obtain substantial value, companies really need to access relevant data without getting overwhelmed with the need to collect and store every piece of data. The size can be daunting, but it’s often the integration of data from multiple sources and formats (variety) and the rapid real-time capture of the data (velocity) that contributes the most substantial value.

Big Data is only valuable if it leads to meaningful actions. Irrespective of how big your data sets are, the goal is to extract intelligence from the data and then be able to take action based on the insights it provides. Therefore, being able to access relevant data, regardless of its source, is critical for any data mining effort. The key is not to get caught up in the volumes. In fact, taking incremental, easily manageable steps when embarking on a Big Data project is perfectly acceptable,as long as you are closely aligned with the company’s business objectives. Start by clearly outlining the goals of your Big Data initiative. What hypothesis are you trying to validate or what type of insights are you looking for and for what purpose. Think about what data is needed and why, who will use it and what are the most relevant data sources that will get you what you really need – just focus on the data that provides the most value, in-house or outside your firewall.

Put Your Big Data into Action There is an unlimited and ever growing number of data sources that house valuable insights about your business, products, customers, competitors, market trends and financial predictors. These include the long trail of social media, review sites and news sources, your cloud applications, as well as government web-based applications (Federal regulations, public data on housing, marriages, foreclosures etc.), channels, suppliers and competitor’s sites. A majority of these data sources are difficult to access and the data they contain is constantly changing. You will need the ability to access a wide variety of data, and to access it in real-time. With a real-time integration platform you can flexibly define and update your desired data sources and access any data you can see on a website. You can just as easily transform that data, enrich and add structure, perform an operation on it, and automate a resulting action.

Imagine a scenario where you could identify customers’ preferences for products, campaigns, channel contact to better market to them and provide an optimal experience that promotes engagement and loyalty. Or turn blogs, forums and social media commentary as predictors for stock performance. Or perhaps determine exposure, portfolio value at risk and liquidity coverage to determine product mix, markets to exit and fine-tune responses to changes in interest rates.

If you had the opportunity to automatically access any applications or web-based data source, how many strategic Big Data initiatives could you think of that will make you a market leader?

For the organizations that will strategically embrace Big Data, the possibilities for innovation, business agility and increased profitability are endless. At the heart of this is,understanding customers, competitors and behaviour, to extract meaningful insights that you can act on, setting you apart from your competition.Don’t be intimidated by the volumes; start your Big Data initiatives by focusing on the data that matters regardless of the data source, type or format in order to immediately start generating meaningful intelligence and taking action.